Method for monitoring an electric motor and detecting a departure from normal operation

ABSTRACT

A method for detecting a departure from normal operation of an electric motor comprises the steps of modelling a set of normal current measurements and a set of operational current measurements for the motor being monitored. The modelling is carried out by a neural network auto-associator which is trained to reproduce its inputs on its output. A potential failure is indicated whenever the set of normal current measurements and the set of operational current measurements differ by more than a predetermined criterion.

This is a divisional of application Ser. No. 08/269,465 filed Jun. 30,1994, now U.S. Pat. No. 5,574,387 issued Nov. 12, 1996 .

The present invention relates to an apparatus and method for indicatingand diagnosing faults in electrodynamic machinery. More specifically,the invention relates further to a system for monitoring an electricinduction motor and for indicating whether the motor is functioningproperly or that it contains an internal fault which will lead tomechanical breakdown. The invention further relates to subsystems usedin such a system and more particularly to that portion of the systemwhich processes a set of electric current measurements obtained byanother portion of the system and produces an indication of "good" or"bad" if the motor appears to be functioning normally or abnormally,respectively.

Reference is made to the following applications with closely relatedsubject matter whereof the disclosure is herein incorporated byreference: A NEURAL NETWORK AUTOASSOCIATOR AND METHOD FOR INDUCTIONMOTOR MONITORING, filed on even date herewith in the names of ThomasPetsche and Stephen Hanson and METHOD AND APPARATUS FOR PREDICTINGELECTRIC INDUCTION MACHINE FAILURE DURING OPERATION, Ser. No.08/251,814, filed on May 31, 1994 in the names of S. Farag, J. Schlag,T. Habetter, and B. Lin.

Electric motors are used extensively in a wide range of articles ofmanufacture and are a common component in much industrial and consumerequipment and in numerous installations and vehicles. Electric motorsare utilized in many applications where they are expected to run withminimal attention and to provide reliable service. Depending on theapplication, the failure of an electric motor in service can possiblylead to an undesirable situation characterized by inconvenience,economic dislocation, expensive industrial consequences and/or, in someapplications, hazardous situations. It is therefore important that faultdiagnosis be done efficiently and automatically where possible. Absentspecial monitoring techniques, some electric motor failure modes andmechanisms tend to be insidious in their onset. For example, bearingproblems, such as may occur through lubrication failure or other causemay not manifest themselves to an equipment operator until a seriousfault situation has developed. For another example, insulation failuresand other electrical problems may not become apparent until irreversibledamage has occurred. And yet, because of the competitiveness of themature market in electric motors, fault detection must be performedeconomically.

In the testing of motors, it is known to provide apparatus which willindicate electrically selected characteristics of motors, as described,for example in U.S. Pat. No. 3,052,117, entitled MOTOR TESTING APPARATUSissued Sep. 4, 1962 in the name of Miller et al. Monitoring andanalyzing the "noise signature" of electric motor-operated devices isalso known in the art. Thus, in U.S. Pat. No. 4,965,513, entitled MOTORCURRENT SIGNATURE ANALYSIS METHOD FOR DIAGNOSING MOTOR OPERATED DEVICESissued Oct. 23, 1990 in the name of Haynes et al, it is disclosed thatthe signature may be recorded and compared with subsequent signatures todetect operating abnormalities and degradation.

Current sensing may be used in this procedure. U.S. Pat. No. 4,743,818entitled MICROPROCESSOR BASED MOTOR PROTECTIVE RELAY WITH ROTORTEMPERATURE DETECTOR, issued May 10, 1988 in the name of Quayle et al,discloses a motor protection relay in which the temperature of arotating motor is inferred from the current flow in the lines whichsupply the motor and from resistance temperature detection devices inthe stator, under microprocessor control.

U.S. Pat. No. 4,647,825 entitled UP-TO-SPEED ENABLE FOR JAM UNDER LOADAND PHASE LOSS, issued Mar. 3, 1987 in the name of Profio et al,discloses a motor controller with a device responsive to a motorcondition for preventing termination of power to the motor in responseto fault conditions when these fault conditions occur during thestart-up of the motor.

In U.S. Pat. No. 4,608,619 entitled GROUND FAULT VOLTAGE LIMITING FOR ALOCOMOTIVE ELECTRIC TRACTION MOTOR, issued Aug. 26, 1986 in the name ofBomer et al, the traction motors of a locomotive are voltage limited toa variable extent to permit continued motor operation following theoccurrence of moisture induced ground faulting. Heating in operationdries the moisture.

U.S. Pat. No. 4,547,826 entitled GENERALIZED REAL-TIME THERMAL MODEL,issued Oct. 15, 1985 in the name of Premeriani, discloses a real-timethermal model of an induction motor to produce values indicative of thetransient and steady state temperature condition of the motor. Thevalues are compared with predetermined limits to remove power when avalue exceeds its limit in order to prevent damage to the motor.

U.S. Pat. No. 4,544,982 entitled ELECTRICAL EQUIPMENT PROTECTIONAPPARATUS AND METHOD, issued Oct. 1, 1988 in the name of Boothman et al,discloses protection of three phase electrical equipment where acurrent-representative signal is derived for each phase and the threesignals are combined to form an analog composite signal. Heating in theequipment is determined from the signals and when the temperatureexceeds a critical value, power is interrupted. Phase loss and phaseunbalance are determined and the power supply is interrupted when eitherexceeds a critical level.

U.S. Pat. No. 4,467,260 entitled MOTOR CONTROL APPARATUS WITH ROTORHEATING PROTECTION, issued Aug. 21, 1984 in the name of Mallick, Jr. etal discloses a microprocessor controlled motor starter. When the shortterm differential temperature between rotor bars and rotor end bells isexcessive, the motor is shut down.

A solid state thermal overload indicator is disclosed in U.S. Pat. No.3,845,354, entitled SOLID STATE THERMAL OVERLOAD issued Oct. 29, 1974 inthe name of Boothman et al, in which analog means are used foraccounting for the heat energy stored in the motor. An analog of theelectrical thermal characteristics of the load is provided so that itsoperating characteristic more closely resembles the load's thermaldamage curve. The patent discloses an indicator for a motor load whichhas independently adjustable stall operating time and running overloadtrip level.

U.S. Pat. No. 3,809,960, entitled HEATING INDICATING DEVICE OF ANELECTRIC MOTOR issued May 7, 1974 in the name of Jossic, discloses aheating indicating device for the rotor of an electric motor usingdirect current. A device is disclosed using a resistor in series withthe rotor for monitoring and utilizing the voltage across the resistorto provide for temperature monitoring.

U.S. Pat. No. 5,270,640, entitled issued Dec. 14, 1993 in the name ofKohler et al. discloses a method for detecting an incident failure in amultiphase electric motor, comprising monitoring voltage and currentvalues at each input to the motor, determining negative sequence voltageand current values for each periodic measured input voltage and currentvalue, calculating an effective negative sequence impedane phasor valueangle from each of the determined negative sequence voltage and currentvalues, and comparing the calculated negative sequence impedance phasorangles and/or real and imaginary components over a plurality of periodicmeasurements to detect a change therein, which change is said to beindicative of an incipient failure mode.

A signal processing system is disclosed in U.S. Pat. No. 4,423,458entitled SIGNAL PROCESSING SYSTEM FOR OVERLOAD RELAY OR THE LIKE, issuedDec. 27, 1983 in the name of Stich. A system is used for sensing currentflow in a single phase or polyphase circuit to be protected and derivinga single signal representing composite current flow. The cooling ofprotected circuit elements is represented.

A control capable of monitoring single phase or polyphase current flowis disclosed in U.S. Pat. No. 4,423,459 entitled SOLID STATE CIRCUITPROTECTION SYSTEM AND METHOD, issued Dec. 27, 1983 in the name of Stichet al. Current imbalance is recognized and compensated.

A control is disclosed in U.S. Pat. No. 4,446,498 entitled ELECTRONICCONTROL SYSTEM FOR OVERLOAD RELAY OR THE LIKE, issued May 1, 1984 in thename of Stich, for monitoring single phase or polyphase current flow. Aninterrupter is tripped when an overcurrent condition occurs.

A method and apparatus for detecting axial cracks in rotors for rotatingmachinery are disclosed in U.S. Pat. No. 4,751,657 entitled METHOD ANDAPPARATUS FOR DETECTING AXIAL CRACKS IN ROTORS FOR ROTATING MACHINERY,issued Jun. 14, 1988, in the name of Imam et al.

In accordance with an aspect of the invention, a method for detecting adeparture from normal operation of an electric motor comprises obtaininga set of normal current measurements for a motor being monitored;training a neural network auto-associator using the set of normalcurrent measurements; making current measurements for the motor inoperation; comparing the current measurements with the normal currentmeasurements; and indicating abnormal operation whenever the currentmeasurements deviate significantly the normal current measurements.

In accordance with another aspect of the invention, the method models aset of normal current measurements for the motor being monitored, andindicates a potential failure whenever measurements from the motordeviate significantly from a model.

In accordance with an aspect of the invention, apparatus for detecting adeparture from normal operation of an electric motor, comprises: sensingapparatus for measuring a set of current values for a motor beingmonitored; first processing apparatus coupled to the sensing apparatusfor deriving frequency spectral components associated with the set ofcurrent values; a neural network auto-associator coupled to the signalprocessing apparatus and further coupled to the sensing apparatus forreceiving at least one of at least a portion of the spectral componentsand at least a portion of the current values as an input vector andhaving output terminals for providing an output vector, the neuralnetwork having undergone a training phase; and second processingapparatus coupled to the output terminals for comparing the input andoutput vectors for providing an error metric, wherein the firstprocessing apparatus clusters the frequency spectral components into asmaller number of clusters during the training phase.

In accordance with another aspect of the invention, the first processingapparatus performs a Fast Fourier Transform (FFT).

In accordance with a further aspect of the invention, the current valuescorrespond to instantaneous values of current.

In accordance with a further aspect of the invention, the clustering isperformed by use of a K-apparatus clustering algorithm.

In accordance with a further aspect of the invention, apparatus fordetecting a departure from normal operation of an electric motor,comprises sensing apparatus for measuring a set of current values for amotor being monitored; first processing apparatus coupled to the sensingapparatus for deriving frequency spectral components associated with theset of current values in accordance with a Fast Fourier Transform (FFT),for scaling the frequency spectral components in accordance withpredetermined weights; a neural network auto-associator coupled to thesignal processing apparatus and further coupled to the sensing apparatusfor receiving at least one of a selected portion of the frequencyspectral components and a portion of the current values as an inputvector and having output terminals for providing an output vector, andincluding a hidden layer, the autoassociator having been trained in atraining phase, using a set of current values obtained from a motorknown to be operating normally, the first processing apparatus havingclustered the frequency spectral components into a smaller number ofclusters during the training phase; and second processing apparatuscoupled to the output terminals for comparing the input and outputvectors for providing an error metric.

In accordance with another aspect of the invention, Φ is defined as thei^(th) component of the frequency spectral components (FFT components),and for each FFT component, the first processing apparatus provides aquantity x_(i), where ##EQU1## where τ is a user defined index set thatselects some subset of the components of the FFT for defining theportion of the frequency spectral components, τ containing L elements,the L elements being normalized with respect to the largest and smallestelements thereof, a resulting set of L scaled magnitude components x₀, .. . , X_(L-1), being defined to represent the maximum RMS value of thecurrent values being monitored.

In accordance with another aspect of the invention, a method fordetecting a departure from normal operation of an electric motor,comprises:

obtaining a set of current measurements for a motor being monitored, themotor being known to be operating normally;

applying the set of current measurements to neural networkauto-associator and training the neural network auto-associator usingthe set of normal current measurements;

making a set of operational current measurements for the motor in actualoperation;

forming clusters of operational current measurements;

applying the clusters of operational current measurements to the neuralnetwork auto-associator;

comparing the set of clusters of current measurements with the output ofthe neural network auto-associator;

indicating abnormal operation whenever the comparing produces a resultin accordance with predetermined criteria.

In accordance with still a further aspect of the invention, a method fordetecting a departure from normal operation of an electric motor,comprises:

obtaining a set of current measurements for a motor being monitoredduring a training phase, the motor being known to be operating normally;

processing the set of current measurements so as to provide currentmeasurement training vectors;

forming clusters of the current measurement vectors during the trainingphase;

applying the clusters of current measurement vector to a neural networkauto-associator and training the neural network auto-associator usingthe set of normal current measurements;

making a set of operational current measurements for the motor in actualoperation;

processing the of operational current measurements so as to provide anoperational current measurement vector;

applying the operational current measurement vector to the neuralnetwork auto-associator;

comparing the set of current measurements with the output of the neuralnetwork auto-associator; and

indicating abnormal operation whenever the comparing produces a resultin accordance with predetermined criteria.

For a further understanding of the present invention, reference is madeto the following description of an exemplary embodiment thereof,considered in conjunction with the accompanying drawings, in which:

FIG. 1 is a general block diagram depicting an artificial neural networkautoassociator according to the present invention; and

FIG. 2 is a block diagram showing an artificial neural network coupledto a computational element which computes the FFT of a set of recentcurrent measurements and from whose output it receives its input.

The term neural network or, more properly, artificial neural network(ANN), has come to mean any computing architecture that consistsessentially of massively parallel interconnections of simple "neural"processors. Neural networks are used in pattern classification bydefining non-linear regions in the feature space. While neural networksare relatively new in regard to operation, they have been investigatedby researchers for many years. For example, see The Proceedings Of TheIEEE, September 1990, Special Issue on Neural Networks, Vol. 8, No. 9,pp. 1409-1544. For a further example, reference is made to pending U.S.patent application Ser. No. 07/899,808 filed Jun. 17, 1992 in the nameof Marcantonio for ARTIFICIAL NEURAL NETWORK (ANN) CLASSIFIER APPARATUSFOR SELECTING RELATED COMPUTER ROUTINES AND METHODS and whereof thedisclosure is herein incorporated by reference. Thus, neural networks ofmany different types of configurations and uses are well known.

The basic building block used in many neural networks is the adaptivelinear element. This is an adaptive threshold logic element whichcomprises an adaptive linear combiner cascaded with a hard limitingquantizer which is used to produce a binary output. In single elementneural elements, an adaptive algorithm is often used to adjust theweights of the adaptive linear element so that it responds correctly toas many patterns as possible in a training set that has binary desiredresponses. Once the weights are adjusted, the responses of the trainedelement can be tested by applying various input patterns. If theadaptive linear element responds correctly with high probability toinput patterns that were not included in the training set, it is saidthat "generalization" has taken place. Thus, learning and generalizationare among the most useful attributes of adaptive linear elements inneural networks. A single adaptive linear element is capable ofrealizing only a small subset of certain logic functions known as thelinearly separable logic functions or threshold logic functions. Theseare the set of logic functions that can be obtained with all possibleweight variations. Thus, such classifiers are sometimes referred to aslinear classifiers. The linear classifier is limited in its capacity andis limited to only linearly separable forms of pattern discrimination.

More sophisticated classifiers with higher capacities are non-linear. Oftwo types of non-linear classifiers which are known in the art, thefirst is a fixed pre-processing network connected to a single adaptiveelement and the other is the multi-element feed forward neural network.These networks, as well as operations and configurations thereof, areknown in the prior art and are described, for example, in theabove-noted publication in an article entitled "Thirty Years of AdaptiveNeural Networks: Perception, Madaline, and Backpropagation" by BernardWidrow et al. in Proceedings of the IEEE, Vol. 78, No. 9, September,1990, pp. 1415-1441. The article, besides describing the history ofneural networks as of the publication date, also has an extensivebibliography in regard to the same. The teaching of this article ishereby incorporated by reference, as is also the earlier citedliterature, as background material to the present invention.

In accordance with the invention, the model takes the form of an neuralnetwork auto-associator which is "trained"--using current measurementscollected while the motor is known to be in a normal operatingcondition--to reproduce the inputs on the output. A new set of currentmeasurements are classified as "good" or "bad" by first transforming themeasurement using a Fast Fourier Transform (FFT) and an internal scalingprocedure, and then applying a subset of the transformed measurements asinputs to the neural network auto-associator. A decision is generatedbased on the difference between the input and output of the network.

Use of the FFT causes a fault to introduce or increase the magnitude ofa narrow bandwidth oscillation in the current. This is seen in the FFTas an increase in the magnitude of the spectrum in a narrow range offrequencies. The particular "internal scaling procedure" is utilizedbecause the inventors have found that, given a set of FFTs which thenetwork was to be trained to recognize, it was more effective to scaleeach FFT individually rather than to apply the same scale factors to allthe FFTs. It is herein recognized that this suggests that the shape ofthe spectrum is more important than the absolute magnitudes, though afacile explanation of why it works better is not apparent at this point.

A decision is generated based on the difference between the input andoutput of the network. The network is "trying" to reproduce the inputvector. The "classification" is then done by comparing the input andoutput vectors and "measuring the difference". A useful way to view thetask of the neural net is to consider an example. Thus, given, forexample, a 129 dimensional input vector, the network compresses thisvector into a 20 dimensional vector (on the hidden units) and thenexpands that back onto the 129 dimensional output vector., The"bottleneck" at the hidden layer forces the network to extract only the20 most important directions in the input space. Thus, the output willmost likely never be exactly the same as the input. But, for inputvectors that are "close to" the vectors on which the network wastrained, the Euclidean distance, for example, between the input andoutput vectors will be small.

Thus the "difference between input and output vectors" is a measure ofthe difference between the input vector that the output vector producedby the trained network when that input is applied. Generally herein, theEuclidean distance is used, namely ##EQU2## Nevertheless, other L_(p)norms have been used, e.g. ##EQU3## Also, it is herein recognized thatthe discrimination of the system may be improved if only selectedcomponents are used to compute the difference. In other words, ratherthan summing over all the components, as the d_(p), the sum wouldcontain only a subset of the components and ignore the rest. Again, thisis motivated by the physics of the system, which predicts that thespectrum of a faulty motor will contain narrow peaks in predictablefrequency spectral locations caused by the fault, while peaks in otherlocations are predicted to be due to external noise.

In accordance with an exemplary embodiment of the invention, a labelingprocedure implemented in software will be described as well as anequivalent hardware implementation. The procedure used to initiallyadapt the system to a new motor will then be explained. This sameprocedure is also used to adapt the system to any changes in the load orenvironment. Embodiments herein described have been implemented insoftware that runs on a workstation or PC.

A subsystem is employed which measures the instantaneous current on asingle phase of the power supply to a three phase electric inductionmotor and the RMS current on all three phases. Such a subsystem usesstandard techniques known in the art. The single phase currentmeasurements at time t are herein referred to as c_(t), and the RMScurrent as r_(t).

These measurements are collected beginning at some time arbitrarilycalled 0 (zero). At time K-1, the K measures c₀, . . . , c_(K-1) aretransformed using a Fast Fourier Transform (FFT) to produce an estimateof the magnitudes of the frequency spectrum components of the signal. Inthe implementation of the described embodiment, values have been used ofk=4096 or 16384. Calling the magnitude of the ith component of the FFTφ_(i), this signal is further processed by scaling each component by theinverse of the magnitude of the largest component in the FFT, i.e., theFFT is scaled so that the relative magnitudes remain constant and therange of magnitudes cover 0,1!.

In one instantiation, for each FFT (independent of all other FFTs)##EQU4## is computed, where τ is a user defined index set that selectssome subset of the components of the FFT. Assume τ contains L elements.In other words, the user defines a subset of the components of the FFTsthat are included, the choice of which FFT components being guided bythe physics of the system and experience with similar problems.

The remaining components are discarded. For each subsampled FFT, thesystem finds the largest and the smallest components. The subsampled FFTis then translated and scaled so that the largest component maps to 1,or some other upper bound less than 1 and the smallest to 0 or someother lower bound greater than 0. In the present work, 0.98 and 0.02,respectively have frequently been used.

Calling the resulting set of L scaled magnitude components x₀, . . . ,x_(L-1), r_(max) is defined to be the maximum RMS current. The followingare defined: ##EQU5## and X=x₀, . . . , x_(L). Thus, the vector Xconsists of L scaled components of the FFT plus the scaled RMS current.

The vector X is applied as input to a neural network with a singlehidden layer of radial basis function units and an output layer oflinear units. The basis functions are spherical gaussians with variablecenter and width. The network can be described as: ##EQU6## Where Y₁(i=0, . . . , L) is the ith output node of the network; H is the numberof nodes in the hidden layer; w_(i),j is a weight on the output ofhidden unit j when applied as input to output unit i; Θ is the thresholdfor output unit i; and σ(u)=(1+e_(-u))₋₁ where e is the base of thenatural logarithm. (In FIG. 1, f(x) exp {(X-c_(j))² /σ_(j) ² }and g=σ.)Other functions for f and g may also be used. c_(j) is the center ofhidden unit j and σ_(j) is its width.

Given the x_(i) and x_(i), an error metric ##EQU7## is computed.

This same procedure is repeated a total of T times to generate T errormetrics d₁, . . . d_(T). The system then indicates that the motor is"good" if ##EQU8## and that it is bad otherwise.

The entire labeling procedure then begins again.

A hardware implementation of this labeling procedure is shown in FIG. 1.Other implementations are possible.

The uniqueness and effectiveness of the motor monitoring system inaccordance with the present invention stems from its ability to adapt tothe particular motor that it is monitoring. This means that when thesystem is first installed, the w_(i),j, q_(j), c_(j), s_(j) ² are allrandomly chosen or completely unknown when the system is exposed to amotor for the first time. The problem the system faces is to estimategood values for these parameters. Further, if the environment or theload that the motor is driving changes, the system will need tore-estimate these parameters.

The parameters are estimated by collecting a large set of currentmeasurements during the period immediately after the monitor isinstalled and while the motor is assumed to be "healthy", that is, ingood running order. It is expected that a motor can be sampledperiodically over the course of a week to generate this training set.The data is then segmented into vectors of length K (as above) and thesame preprocessing--i.e., FFT and rescaling--as above is applied to eachvector. For ease of explanation, assume that K * M current measurementsc₀, . . . , C_(KM-1) have been collected and processed to produce Mvectors X¹, . . . , X^(M).

The first step in training the network is to place the centers of theradial basis functions near the input data. A K-means clusteringalgorithm is herein used to group the M input measurements into Hclusters. K-means works by ransom;y selecting H inputs as the initialcluster centers. Then it assigns every input to the nearest clustercenter and moves each cluster center to the mean position of thatcluster's members. The two steps of assigning to clusters and moving vcluster centers repeat until no points change clusters during theassignment phase. After the clustering, the basis function widths,σ_(j), are set to the Euclidian distance from cluster j to its nearestneighbor. The output layer weights and thresholds are initialized to 0.The present invention differs specifically from the aforementionedco-pending application in the K-means clustering is necessary for theradial basis function network to operate properly and cannot be used inthe network of the aforementioned application.

After the initialization phase, any one of many optimization algorithms,usually simple gradient descent, or aka error back propagation, may beused to choose the weights and thresholds in the neural network tominimize the mean square error, ##EQU9##

Given these estimates of the network parameters, on the same trainingset or another one, ##EQU10## is computed for j=1, . . . , N. Then, anestimate is made of Θ as Θ=μ+as, where ##EQU11## and a is a userdefinable parameter, herein taken to be 1.

As an alternative, order statistics can also be used to estimate Θ suchthat Θ=m+ar where m is the median of the d_(i) and r is the distancebetween the upper and lower quartiles of the d_(i) . (The median of aset of numbers x₁, . . . , x_(N), is x* if the number of x_(i) such thatx_(i) ≧x* is the same as the number of x_(i) such that x_(i) ≦x*. Theupper quartile of the set is x_(u) such that 1/4 of the x_(i) aregreater than x_(u) and 3/4 are less than x_(u). The lower quartile ofthe set is x_(u) such that 1/4 of the x_(i) are less than x_(u) and 3/4are greater than x_(u).) Again, a is a user definable parameter. Someother range for R can also be used such as the smallest range thatincludes all the data except the upper and lower 10%.

FIG. 1 illustrates what is generally known as a neural networkautoassociator. The input and output layers each contain the same numberof units. The task of an autoassociator is to reproduce the input vectoron the output units (i.e., do not label the outputs "1 of n"). If therewere no hidden layer, or the hidden layer contained as many units as theinput and output layers, this task could be performed by a simpleidentity mapping from each layer to the next. However, since there arefewer hidden units than input units, the system must compress the inputvectors and then expand them onto the output. Thus the hidden layer canrepresent only a subset of the input space.

As will be seen, each of the modules referenced by numerals 10, 11 and12 and the remaining modules pertain to processing elements which appearin the system. The connections between the modules represent theconnection weights, as is well known. Thus, for example, each of themodules 10, 11 and 12 may represent first layer adaptive linearelements, second layer or third layer adaptive linear elements with eachof the output stages, such as 12, representing an output layer adaptivelinear element or a trainable layered neural network with multipleadaptive elements.

Referring to FIG. 2, digitized single phase motor current measurementsare applied to a signal processing unit 4 by way of a series of delayelements, generally indicated as 6. The motor and signal sensingarrangements are conventional and are not shown. For example, currenttransformers or sensing resistors and the like may be used. Signalprocessing unit 4 performs appropriate magnitude rescaling and derivesthe Fast Fourier Transform of the applied signals. The outputs of signalprocessing unit 4 are applied to a series of input processing elements10 of a neural network classifier such as was shown in FIG. 1. Theoutputs of the class modules, 12 through 18 are each directed to acomputation network indicated as 20. Computation network 20 takes asinput the inputs to the neural network and the outputs of the networkand produces as output a metric representing the difference between theinput and output vectors. For example, if Euclidean distance is beingused and the input vector is X₁, . . . , X_(n), and the output vector isY₁ . . . Y_(n) the output of "A" is the value ##EQU12## The boxeslabeled 6, containing the character are for providing delays, so theadder 22 takes as input a set of the k most recent values of thedifference between the input and output of the neural network andproduces as output either the sum or average (equivalently) of thesevalues. The comparator 24 then compares this sum or average to athreshold and then indicates that the motor is good or bad depending onwhether the input is above or below the threshold, respectively.

As one can understand, the invention has great applicability and theessence of the invention is quite general in its applicability and canbe used in a wide variety of neural network classifier implementations.While the invention has been explained by way of specific, exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes and modifications can be incorporated without departingfrom the spirit and scope of the invention which is defined by theclaims following. Thus, while current sampling relating to a singlephase has been referred to, the method is clearly applicable to singlephase and polyphase apparatus. Furthermore, it is not essential that thesupply current itself be monitored: it is entirely within thecontemplation of the invention to monitor auxiliary currents whichnevertheless incorporate information relating to motor condition. Themethod is generally applicable to electrodynamic machinery, includingmotors and generating equipment. Clearly, the method is suitable forimplementation by general purpose computer, dedicated computer, or byspecialized electronic circuitry.

What is claimed is:
 1. A method for detecting a departure from normal operation of an electric motor, comprising:modelling a set of normal current measurements for a motor being monitored; modelling a set of operational current measurements for said motor being monitored, wherein said modelling is carried out by a neural network auto-associator which is trained to reproduce its inputs on its output; indicating a potential failure whenever said set of normal current measurements and said set of operational current measurements differ by more than a predetermined criterion.
 2. A method for detecting a departure from normal operation of an electric motor in accordance with claim 1 wherein new sets of current measurements are classified as "good" or "bad" by first transforming the measurement using a Fast Fourier Transform (FFT) and an internal scaling procedure, and then applying a subset of the transformed measurements as inputs to the neural network auto-associator, whereby a decision is generated based on the difference between the input and output of the network.
 3. A method for detecting a departure from normal operation of an electric motor in accordance with claim 2 wherein said neural network auto-associator is trained utilizing current measurements collected using an electric motor known to be in a normal operating condition.
 4. A method for detecting a departure from normal operation of an electric motor in accordance with claim 3 wherein said subset of the transformed measurements is user-definable. 